{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Horse-or-Human-WithAugmentation.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RXZT2UsyIVe_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "8466a527-9022-446d-a32f-8389a4ec1e70"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \\\n",
        "    -O /tmp/horse-or-human.zip\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip \\\n",
        "    -O /tmp/validation-horse-or-human.zip\n",
        "  \n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/tmp/horse-or-human.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/horse-or-human')\n",
        "local_zip = '/tmp/validation-horse-or-human.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/validation-horse-or-human')\n",
        "zip_ref.close()\n",
        "# Directory with our training horse pictures\n",
        "train_horse_dir = os.path.join('/tmp/horse-or-human/horses')\n",
        "\n",
        "# Directory with our training human pictures\n",
        "train_human_dir = os.path.join('/tmp/horse-or-human/humans')\n",
        "\n",
        "# Directory with our training horse pictures\n",
        "validation_horse_dir = os.path.join('/tmp/validation-horse-or-human/horses')\n",
        "\n",
        "# Directory with our training human pictures\n",
        "validation_human_dir = os.path.join('/tmp/validation-horse-or-human/humans')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-06-15 04:27:18--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 216.58.203.240, 2404:6800:4003:c01::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|216.58.203.240|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 149574867 (143M) [application/zip]\n",
            "Saving to: ‘/tmp/horse-or-human.zip’\n",
            "\n",
            "/tmp/horse-or-human 100%[===================>] 142.65M  40.4MB/s    in 3.5s    \n",
            "\n",
            "2019-06-15 04:27:22 (40.4 MB/s) - ‘/tmp/horse-or-human.zip’ saved [149574867/149574867]\n",
            "\n",
            "--2019-06-15 04:27:25--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.194.128, 2404:6800:4003:c01::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.194.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11480187 (11M) [application/zip]\n",
            "Saving to: ‘/tmp/validation-horse-or-human.zip’\n",
            "\n",
            "/tmp/validation-hor 100%[===================>]  10.95M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2019-06-15 04:27:25 (121 MB/s) - ‘/tmp/validation-horse-or-human.zip’ saved [11480187/11480187]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5oqBkNBJmtUv"
      },
      "source": [
        "## Building a Small Model from Scratch\n",
        "\n",
        "But before we continue, let's start defining the model:\n",
        "\n",
        "Step 1 will be to import tensorflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvfZg3LQbD-5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BnhYCP4tdqjC"
      },
      "source": [
        "We then add convolutional layers as in the previous example, and flatten the final result to feed into the densely connected layers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gokG5HKpdtzm",
        "colab_type": "text"
      },
      "source": [
        "Finally we add the densely connected layers. \n",
        "\n",
        "Note that because we are facing a two-class classification problem, i.e. a *binary classification problem*, we will end our network with a [*sigmoid* activation](https://wikipedia.org/wiki/Sigmoid_function), so that the output of our network will be a single scalar between 0 and 1, encoding the probability that the current image is class 1 (as opposed to class 0)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PixZ2s5QbYQ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "fa108f9e-e196-40ba-d3f7-8558e4aa6cfc"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    # Note the input shape is the desired size of the image 300x300 with 3 bytes color\n",
        "    # This is the first convolution\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    # The second convolution\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The third convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The fourth convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The fifth convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # Flatten the results to feed into a DNN\n",
        "    tf.keras.layers.Flatten(),\n",
        "    # 512 neuron hidden layer\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0615 04:27:31.908260 140291060316032 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8DHWhFP_uhq3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "20ffef33-09a3-436c-97af-5de8fe821d6d"
      },
      "source": [
        "from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=RMSprop(lr=1e-4),\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0615 04:27:32.169507 140291060316032 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ClebU9NJg99G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5d6bcb40-92b4-42a8-cf45-c57fc1b93973"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# All images will be rescaled by 1./255\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "\n",
        "validation_datagen = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "# Flow training images in batches of 128 using train_datagen generator\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        '/tmp/horse-or-human/',  # This is the source directory for training images\n",
        "        target_size=(300, 300),  # All images will be resized to 150x150\n",
        "        batch_size=128,\n",
        "        # Since we use binary_crossentropy loss, we need binary labels\n",
        "        class_mode='binary')\n",
        "\n",
        "# Flow training images in batches of 128 using train_datagen generator\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        '/tmp/validation-horse-or-human/',  # This is the source directory for training images\n",
        "        target_size=(300, 300),  # All images will be resized to 150x150\n",
        "        batch_size=32,\n",
        "        # Since we use binary_crossentropy loss, we need binary labels\n",
        "        class_mode='binary')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1027 images belonging to 2 classes.\n",
            "Found 256 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Fb1_lgobv81m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3417
        },
        "outputId": "f9110f13-a717-4221-b3b7-684aec9da9b4"
      },
      "source": [
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=8,  \n",
        "      epochs=100,\n",
        "      verbose=1,\n",
        "      validation_data = validation_generator,\n",
        "      validation_steps=8)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.6745 - acc: 0.5740 - val_loss: 0.6673 - val_acc: 0.7734\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.6767 - acc: 0.7141 - val_loss: 0.6733 - val_acc: 0.5195\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.6140 - acc: 0.6774 - val_loss: 0.6517 - val_acc: 0.5820\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 41s 5s/step - loss: 0.5702 - acc: 0.7080 - val_loss: 0.8061 - val_acc: 0.5000\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.6177 - acc: 0.6796 - val_loss: 0.8895 - val_acc: 0.5000\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.5270 - acc: 0.7383 - val_loss: 0.6647 - val_acc: 0.5781\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.6270 - acc: 0.6641 - val_loss: 0.9897 - val_acc: 0.5000\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.5146 - acc: 0.7397 - val_loss: 0.8582 - val_acc: 0.5195\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.5524 - acc: 0.7375 - val_loss: 0.8839 - val_acc: 0.5234\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.5310 - acc: 0.7575 - val_loss: 0.9447 - val_acc: 0.5195\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.5559 - acc: 0.7464 - val_loss: 0.9189 - val_acc: 0.5195\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.4844 - acc: 0.7697 - val_loss: 1.0005 - val_acc: 0.5312\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.5273 - acc: 0.7675 - val_loss: 1.2102 - val_acc: 0.5195\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.4879 - acc: 0.7898 - val_loss: 1.2250 - val_acc: 0.5195\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.4444 - acc: 0.8164 - val_loss: 1.1449 - val_acc: 0.5312\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.5289 - acc: 0.7508 - val_loss: 1.2383 - val_acc: 0.5195\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.4279 - acc: 0.7786 - val_loss: 1.1913 - val_acc: 0.5273\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.4350 - acc: 0.8098 - val_loss: 1.4619 - val_acc: 0.5078\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.3766 - acc: 0.8265 - val_loss: 1.4201 - val_acc: 0.5312\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.4670 - acc: 0.7675 - val_loss: 1.3671 - val_acc: 0.5352\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.3862 - acc: 0.8223 - val_loss: 1.5722 - val_acc: 0.5195\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.4147 - acc: 0.7739 - val_loss: 1.4466 - val_acc: 0.5430\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.4110 - acc: 0.8176 - val_loss: 2.0012 - val_acc: 0.5078\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.3404 - acc: 0.8564 - val_loss: 1.7137 - val_acc: 0.5352\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.3527 - acc: 0.8576 - val_loss: 1.6605 - val_acc: 0.5469\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.3456 - acc: 0.8343 - val_loss: 1.3148 - val_acc: 0.5703\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.3735 - acc: 0.8465 - val_loss: 1.5808 - val_acc: 0.5469\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2917 - acc: 0.8532 - val_loss: 1.9444 - val_acc: 0.5195\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.4298 - acc: 0.8432 - val_loss: 1.4620 - val_acc: 0.5625\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2885 - acc: 0.8643 - val_loss: 2.0795 - val_acc: 0.5195\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.4100 - acc: 0.8320 - val_loss: 2.5562 - val_acc: 0.5078\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.3305 - acc: 0.8754 - val_loss: 3.2307 - val_acc: 0.5000\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 39s 5s/step - loss: 0.3451 - acc: 0.8496 - val_loss: 2.0764 - val_acc: 0.5156\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2953 - acc: 0.8765 - val_loss: 2.2732 - val_acc: 0.5117\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.3757 - acc: 0.8598 - val_loss: 2.2634 - val_acc: 0.5117\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2649 - acc: 0.8665 - val_loss: 2.0424 - val_acc: 0.5352\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2767 - acc: 0.8687 - val_loss: 2.2805 - val_acc: 0.5117\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 30s 4s/step - loss: 0.3145 - acc: 0.8465 - val_loss: 2.6290 - val_acc: 0.5078\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 38s 5s/step - loss: 0.2586 - acc: 0.8848 - val_loss: 3.2814 - val_acc: 0.5000\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 29s 4s/step - loss: 0.4165 - acc: 0.8049 - val_loss: 2.4788 - val_acc: 0.5078\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 38s 5s/step - loss: 0.2367 - acc: 0.9160 - val_loss: 2.6254 - val_acc: 0.5078\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 30s 4s/step - loss: 0.2674 - acc: 0.8837 - val_loss: 1.6300 - val_acc: 0.5977\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 38s 5s/step - loss: 0.3060 - acc: 0.8672 - val_loss: 2.2404 - val_acc: 0.5312\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2788 - acc: 0.8854 - val_loss: 2.8374 - val_acc: 0.5078\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2508 - acc: 0.8954 - val_loss: 2.4864 - val_acc: 0.5195\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.3156 - acc: 0.8799 - val_loss: 4.1207 - val_acc: 0.5000\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.2723 - acc: 0.8865 - val_loss: 3.1216 - val_acc: 0.5117\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.2131 - acc: 0.9199 - val_loss: 2.3614 - val_acc: 0.5625\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2381 - acc: 0.8699 - val_loss: 2.6400 - val_acc: 0.5195\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1952 - acc: 0.9121 - val_loss: 3.6531 - val_acc: 0.5078\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.3179 - acc: 0.8821 - val_loss: 3.1317 - val_acc: 0.5156\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2934 - acc: 0.8877 - val_loss: 3.1612 - val_acc: 0.5117\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 29s 4s/step - loss: 0.2077 - acc: 0.9134 - val_loss: 1.9105 - val_acc: 0.6445\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 38s 5s/step - loss: 0.2267 - acc: 0.9062 - val_loss: 3.1890 - val_acc: 0.5156\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2849 - acc: 0.8977 - val_loss: 2.8577 - val_acc: 0.5156\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.1924 - acc: 0.9177 - val_loss: 3.5242 - val_acc: 0.5156\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 39s 5s/step - loss: 0.2290 - acc: 0.9014 - val_loss: 3.2892 - val_acc: 0.5117\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 30s 4s/step - loss: 0.2851 - acc: 0.8798 - val_loss: 5.6231 - val_acc: 0.5000\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2280 - acc: 0.9066 - val_loss: 3.4384 - val_acc: 0.5234\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1688 - acc: 0.9255 - val_loss: 3.8707 - val_acc: 0.5117\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.2110 - acc: 0.9150 - val_loss: 2.3180 - val_acc: 0.5977\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1929 - acc: 0.9110 - val_loss: 3.3356 - val_acc: 0.5234\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.2830 - acc: 0.8954 - val_loss: 3.6368 - val_acc: 0.5156\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1517 - acc: 0.9422 - val_loss: 4.6208 - val_acc: 0.5117\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1685 - acc: 0.9199 - val_loss: 3.6071 - val_acc: 0.5312\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 39s 5s/step - loss: 0.1953 - acc: 0.9160 - val_loss: 3.2266 - val_acc: 0.5352\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1408 - acc: 0.9399 - val_loss: 4.4980 - val_acc: 0.5156\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2095 - acc: 0.9077 - val_loss: 3.6422 - val_acc: 0.5234\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 29s 4s/step - loss: 0.3200 - acc: 0.8902 - val_loss: 4.2418 - val_acc: 0.5195\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 38s 5s/step - loss: 0.1569 - acc: 0.9395 - val_loss: 4.0331 - val_acc: 0.5234\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.3694 - acc: 0.8921 - val_loss: 3.7823 - val_acc: 0.5312\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.2180 - acc: 0.9166 - val_loss: 3.4553 - val_acc: 0.5312\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.1369 - acc: 0.9433 - val_loss: 3.7999 - val_acc: 0.5312\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 39s 5s/step - loss: 0.1456 - acc: 0.9414 - val_loss: 5.1245 - val_acc: 0.5000\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.1870 - acc: 0.9177 - val_loss: 3.9904 - val_acc: 0.5273\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.3555 - acc: 0.8977 - val_loss: 3.9922 - val_acc: 0.5156\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1543 - acc: 0.9333 - val_loss: 4.3198 - val_acc: 0.5156\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 29s 4s/step - loss: 0.1405 - acc: 0.9419 - val_loss: 3.7046 - val_acc: 0.5312\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.2036 - acc: 0.9522 - val_loss: 1.7309 - val_acc: 0.7070\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 38s 5s/step - loss: 0.1935 - acc: 0.9229 - val_loss: 4.4832 - val_acc: 0.5234\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2015 - acc: 0.9210 - val_loss: 3.7936 - val_acc: 0.5312\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1817 - acc: 0.9210 - val_loss: 3.9576 - val_acc: 0.5312\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2514 - acc: 0.9132 - val_loss: 3.5171 - val_acc: 0.5703\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.1394 - acc: 0.9453 - val_loss: 4.4892 - val_acc: 0.5195\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 29s 4s/step - loss: 0.2160 - acc: 0.9173 - val_loss: 3.1654 - val_acc: 0.5469\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.1378 - acc: 0.9492 - val_loss: 3.8428 - val_acc: 0.5352\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.1335 - acc: 0.9499 - val_loss: 4.5758 - val_acc: 0.5234\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1293 - acc: 0.9410 - val_loss: 4.8085 - val_acc: 0.5234\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.1612 - acc: 0.9333 - val_loss: 3.7595 - val_acc: 0.5352\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2024 - acc: 0.9266 - val_loss: 4.3437 - val_acc: 0.5234\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.2118 - acc: 0.9199 - val_loss: 4.0701 - val_acc: 0.5312\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1966 - acc: 0.9232 - val_loss: 5.1493 - val_acc: 0.5117\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 40s 5s/step - loss: 0.1232 - acc: 0.9502 - val_loss: 3.6931 - val_acc: 0.5625\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 29s 4s/step - loss: 0.1398 - acc: 0.9406 - val_loss: 4.4028 - val_acc: 0.5312\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1141 - acc: 0.9511 - val_loss: 4.8011 - val_acc: 0.5156\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1499 - acc: 0.9344 - val_loss: 5.0668 - val_acc: 0.5117\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 38s 5s/step - loss: 0.1450 - acc: 0.9404 - val_loss: 4.3429 - val_acc: 0.5234\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.1058 - acc: 0.9633 - val_loss: 5.3818 - val_acc: 0.5117\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.2562 - acc: 0.9255 - val_loss: 2.5579 - val_acc: 0.6133\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.1412 - acc: 0.9477 - val_loss: 3.2613 - val_acc: 0.5742\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zNPRWOVJdOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "0247b2d4-e86b-40f0-c4a7-8e8e599e88ec"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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axxe9+mo2eC4PjLA4bx6t60aNmKXxmGM44HS1ahzEwk+tWpGTbPXpQz/8888z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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3ggR9nN27+XrpLBpviIoYt1IqG8AVAN4IbnMEwTPaRdqZ/STYWIVKampc1xlx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\n3M2pgK7Q4rxokbFutqNmo/P8j+bZ3CM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4XyM9ncV/zpza39u4kc/l3HOJnnuO6Mcfaw/e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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1goKlrEwJwD_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}